Add solution for day 3

Day_3/Solutions/Exercise_2_R_code_2020.R

+#---------------------------------------------------------------------------------------
+# Exercise 2
+#---------------------------------------------------------------------------------------
+
+# Given a list of hits and a background in folder /Exercise 2
+# background.txt and id_list.txt
+
+# perform enrichment analysis against 
+# 1. Gene Ontology,
+# 2. Kegg modules, and
+# 3. DisGeNet (diseases)
+
+# For each result produce a cnetplot and an upsetplot.
+# - you will need to provide the correct id's for the functions to execute - use converters
+# - vary the p-value q-value thresholds in case of an empty result
+# - make the disgenet output readable (gene names, not entrez id's)
+
+
+background <- read.csv("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 Online Course Series/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/Exercise 2 (custom background)/Background.txt", sep="")
+id_list <- read.delim("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 Online Course Series/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/Exercise 2 (custom background)/id_list.txt")
+
+
+
+keytypes(org.Hs.eg.db)
+entrez_map <- bitr(background$GeneID, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db", drop= FALSE)
+
+
+# or biomart:
+
+# you will need to convert identifiers - for that purpose I suggest using the biomaRt package in R, alternatively you can use the website itselt
+# ensembl biomart http://www.ensembl.org/biomart/martview/
+library("biomaRt")
+ensembl = biomaRt::useMart("ensembl")
+ensembl = useDataset("hsapiens_gene_ensembl", mart=ensembl)
+datasets_biomart <- listDatasets(ensembl)
+attributes_biomart <- listAttributes(ensembl)
+
+id_list_biomart= getBM(attributes = c("hgnc_symbol","entrezgene_id", "ensembl_gene_id"),
+               filters = c(filters = "entrezgene_id"),
+               values = id_list$EntrezGene.ID,
+               mart = ensembl)
+
+
+background_biomart= getBM(attributes = c("hgnc_symbol","entrezgene_id", "ensembl_gene_id"),
+                          filters = c(filters = "hgnc_symbol"),
+                          values = background$GeneID,
+                          mart = ensembl)
+
+
+# part 1
+id_list_go <- enrichGO(gene = id_list_biomart$entrezgene_id, 'org.Hs.eg.db', pvalueCutoff = 0.05, pAdjustMethod = "BH", universe = names(background_biomart$entrezgene_id), minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.05, readable = TRUE)
+head(id_list_go)
+cnetplot(id_list_go)
+upsetplot(id_list_go)
+
+# part 2 
+id_list_mkegg <- enrichMKEGG(gene = id_list_biomart$entrezgene_id, pvalueCutoff = 0.1, pAdjustMethod = "BH", universe = names(background_biomart$entrezgene_id), minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.1)
+head(id_list_mkegg)
+cnetplot(id_list_mkegg)
+upsetplot(id_list_mkegg)
+
+# part 3
+id_list_dgn <- enrichDGN(gene = id_list_biomart$entrezgene_id, pvalueCutoff = 0.05, pAdjustMethod = "BH", universe = names(background_biomart$entrezgene_id), minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.05, readable = TRUE)
+head(id_list_dgn)
+cnetplot(id_list_dgn)
+upsetplot(id_list_dgn)
+
+# GO without background 
+id_list_go_noBckg <- enrichGO(gene = id_list_biomart$entrezgene_id, 'org.Hs.eg.db', pvalueCutoff = 0.05, pAdjustMethod = "BH", minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.05, readable = TRUE)
+head(id_list_go_noBckg)
+cnetplot(id_list_go_noBckg)
+upsetplot(id_list_go_noBckg)
+
+